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Massachusetts Institute of Technology

Mathematics of Big Data and Machine Learning, IAP 2020

Massachusetts Institute of Technology via YouTube

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Overview

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This course examines mathematical approaches to big data and machine learning, emphasizing data processing and architecture. Topics include associative arrays, structured and unstructured data analysis, entity analysis, biological sequence cross-correlation, and power-law and Kronecker graph generation.

Syllabus

1. Artificial Intelligence and Machine Learning.
2. Cyber Network Data Processing; AI Data Architecture.
Lecture: Mathematics of Big Data and Machine Learning.
0. Introduction.
0. Examples Demonstration.
1. Using Associative Arrays.
1. Examples Demonstration.
2. Group Theory.
2. Examples Demonstration.
3. Entity Analysis in Unstructured Data.
3. Examples Demonstration.
4. Analysis of Structured Data.
4. Examples Demonstration.
5. Perfect Power Law Graphs -- Generation, Sampling, Construction, and Fitting.
5. Examples Demonstration.
6. Bio Sequence Cross Correlation.
6. Examples Demonstration.
Demonstration 7.
7. Kronecker Graphs, Data Generation, and Performance.
7. Examples Demonstration.

Taught by

MIT open courseware

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Reviews

5.0 rating, based on 2 Class Central reviews

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  • Sanzida Kabir Smrity
    good enough for learning AI in order to making learning easier.Now a days AI becomes more relevant for our real life.often people like to use AI instead of human being.As i want to be a teacher so i think it is very essential for me to learning artificial intelligence in more productive way.
  • Anonymous
    Mashinali o'rganishning fundamental matematika qismini o'rganish uchun ajoyib kurs. Ma'lumotlar hajmi va algoritmlarning ishlash prinsiplari aniq tahlil qilingan. MIT materiallari har doimgidek yuqori sifatda. Barcha Data Science va ML yo'nalishidagilarga tavsiya qilaman.
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